\documentclass{njustbeamer}

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\title[How To Write Introduction]{How To Write Introduction}
%\subtitle{Speaker：Weihao Bo}
\author{Group 5}
\institute[NJUST]{School of Computer Science and Engineering}
\date{November 2, 2023}
\begin{document}
\begin{CJK}{UTF8}{gbsn}

%% title frame
\begin{frame}
    \titlepage

    \textbf{Speaker:} Weihao Bo \\
    \textbf{Team Members:} XiangyinHu, MengzeLiu, DiWang, QimengGuo, PanliYuan
\end{frame}

\begin{frame}
    \frametitle{Contents}
    
    \resizebox{!}{0.3\textheight}{ % Adjust the font size by resizing the content
        \begin{minipage}{\textwidth}
            \begin{enumerate}
                \item \textbf{What is an introduction}
                \item \textbf{Function of introduction}
                \begin{itemize}
                    \item Introducing the background
                    \item Showing the research gap
                    \item Stating the general challenging(Motivation)
                    \item Stating the main contribution \& structure of the paper
                \end{itemize}
                \item \textbf{Some examples of featured introduction}
            \end{enumerate}
        \end{minipage}
        }
    
\end{frame}

\begin{frame}[plain]
    \vfill
    \centering
    \begin{beamercolorbox}[sep=4pt,center,colsep=-4bp,rounded=true,shadow=true]{title}
        \Huge What is an introduction
    \end{beamercolorbox}
    \vfill
\end{frame}


\begin{frame}
    \frametitle{What is an introduction}



        \begin{block}{Definition}
            \justifying
            The introduction section is the start of a research article and can be the most challenging part to write. 
            It is generally half a page in length, though it can run longer if the literature review part is integrated. 
            The main purpose of an introduction is to give \textbf{background information} about the \textbf{topic of the paper}, 
            and set out the \textbf{specific questions} to be addressed by the author. 
    
        \end{block}

        \begin{figure}
            \centering
            \includegraphics[scale=0.1]{figures/fig2.png}
        \end{figure}


\end{frame}

\begin{frame}[plain]
    \vfill
    \centering
    \begin{beamercolorbox}[sep=4pt,center,colsep=-4bp,rounded=true,shadow=true]{title}
        \Huge  Functions of introduction
    \end{beamercolorbox}
    \vfill
\end{frame}


\begin{frame}
    \frametitle{Function1: Introducing the background}

    \begin{block}{}
        The research background of the paper is very important, 
        as it is the foundation and prerequisite of the entire paper. 
        Research background can help readers understand the source, 
        development process, current research status, existing problems, 
        and the significance and value of the research, 
        providing necessary background knowledge for understanding the research content. 
        Specifically, the importance of the research background of the paper is reflected in the following aspects:
    \end{block}

    \begin{minipage}{0.4\linewidth} % 40% of the text width
        \begin{itemize}
            \item \justifying Determine research questions
            \item \justifying Positioning research issues
            \item \justifying Explanation of research significance
            \item \justifying Improving credibility
        \end{itemize}
    \end{minipage}
    \hfill % This creates a horizontal fill, pushing the next mini page to the right.
    \begin{minipage}{0.5\linewidth} % 50% of the text width
        \begin{figure}
            \centering
            \includegraphics[scale=0.5]{figures/fig1.png}
        \end{figure}
    \end{minipage}

\end{frame}

\begin{frame}
    \frametitle{Example of function1}
    \textbf{Why do we need to do the work of burned area segmentation?}
    \begin{block}{}
        During the arid season of a year, about 400 Mha of
vegetation-covered land in the world caught on fire [1].
As one of the natural phenomena, vegetation fires can promote
vegetation regrowth, but they can also have a devastating
impact on human infrastructure. In addition, secondary impact
on the environment following fires can pose hazards such as
erosion, flooding, and degradation of water quality [2], [3].
To minimize long-term hazards from vegetation fires, land
management staff typically conduct post-disaster restoration
soon after a fire and within a year [3]. Post-disaster management planning requires a structured assessment of the
landscape mosaic and scale of the fire, \textbf{which includes locating
and estimating the extent of the burned area [4].}
    \end{block}

    W. Bo, J. Liu, X. Fan, et al, "BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022
\end{frame}

\begin{frame}
    \frametitle{Function1: Introducing the background}
    \begin{block}{Useful Sentences}
        \begin{itemize}
            \item Over the past several decades…
            \item The previous work on… has indicated that…
            \item Recent experiments by… have suggested…
            \item Several researchers have theoretically investigated…
            \item In most studies of…, …has been emphasized with attention being given to…
            \item Industrial use of… is becoming increasing common.
            \item There have been a few studies highlighting…
            \item It is well known that…
        \end{itemize}

    \end{block}
\end{frame}

\begin{frame}
    \frametitle{Function2: Showing the research gap}
    \begin{block}{Definition}
        Research Gap refers to the area or topic that has not yet been 
        explored or has not been sufficiently studied in previous research. 

        \begin{itemize}
            \item Determine the research question
            \item Determine the research scope
            \item Determine the research significance 
        \end{itemize}
    \end{block}

    \begin{block}{How to show the research gap}
        \begin{itemize}
            \item Review existing literature
            \item Identify limitations or gaps
            \item Justify research significance
            \item State research objective 
        \end{itemize}
    \end{block}
\end{frame}

\begin{frame}
        \frametitle{Example of Function2}

        \textbf{The remote sensing images of the burned area include satellite images and UAV images.
        This text limits the research scope to UAV images.}
        \begin{block}{}
            Although new satellite sensors offer finer spatial
            scales and faster response capabilities to counter the 
            drawbacks of satellite data being unsuitable for achieving regional
            and local targets, they are still expensive [7]. In contrast,
            unmanned aerial vehicles (UAVs) combine the advantages of
            high resolution, fast turnaround times, and lower operating
            costs with the ability to plan routes flexibly to avoid the
            effects of weather, thus providing wide-ranging applicability
            in post-disaster detection and management [8]. \textbf{Today, remote
            sensing (RS) images acquired by UAVs are used in various
            image-processing tasks [9], [10]. Therefore, it also can be
            applied to the study of fire burned areas [11].}
        \end{block}


    
\end{frame}

\begin{frame}
    \frametitle{Useful Sentences of Function2}
    
    \begin{block}{Introducing the present work}
        \begin{itemize}
            \item In this paper, ... is investigated / studied / discussed /presented.
            \item The present work deals mainly with...
            \item On the basis of existing literature data, we carried out studies in an effort to...
            \item The present study will therefore focus on…
            \item The primary goal / intention of this research is...
            \item The overall objective of this study is...
            \item The work / investigation presented in this paper focuses on…
        \end{itemize}
    \end{block}

\end{frame}

\begin{frame}
    \frametitle{Useful Sentences of Function2}
    \begin{block}{Presenting existing problems}
        \begin{itemize}
            \item Great progress has been made in this field. However, ...
            \item No experiment in this area has suggested that…
            \item Experiments must be initiated to substantiate...
            \item No clear advancement has so far been seen in...
            \item No direct outcome was then reported in...
            \item So far there is not enough convincing evidence showing...
        \end{itemize}
    \end{block}
\end{frame}

\begin{frame}
    \frametitle{Useful Sentences of Function2}

    \begin{block}{Limiting the scope of work}
         \begin{itemize}
                \item The problem under discussion is within the scope of...
                \item Studies of these effects covered various aspects of...
                \item This subject is concerned chiefly with the study of...
                \item The problem I have referred to falls within the field of...
                \item The problem we have just outlined seems to be inside of the province of...
        \end{itemize}
    \end{block}

\end{frame}

\begin{frame}
    \frametitle{Function3: Stating the general challenging(Motivation)}
    \begin{block}{Definition}
        Stating the challenging purpose of the research is one of the functions of introduction. 
        If the first two functions of introduction are to tell the reader where to start, 
        then, this function is to tell the reader where to go. 
        Making clear in the introductory section what the purpose and 
        task of the paper are and emphasizing the primary objectives of
         the research will not only inform readers of the importance of
          the study but also avoid any misunderstanding of the writer's inclination.
    \end{block}
\end{frame}

\begin{frame}
    \frametitle{Example of function3}
    \begin{block}{}
        For example, Fig. 2 shows some
examples of the saliency maps of different methods applied
on the two datasets. \textbf{The first challenge is the large scale of
the burned area. }As shown in Fig. 3(a), due to shooting time,
weather or geography, there are many different interferences
on a large area causing occlusions.
    \end{block}
    \vspace{-0.2cm}
    \begin{figure}
        \centering
        \includegraphics[scale=0.28]{figures/fig3.png}
    \end{figure}
\end{frame}

\begin{frame}
    \frametitle{Example of function3}

    \begin{minipage}{0.4\linewidth} % 40% of the text width
        \begin{block}{}
            \textbf{The second challenge is the complexity of region edges and
    numerous similar patterns of foreground and background} in a
    large natural environment [as illustrated in Fig. 3(b)], posing
    difficulties for neural network learning. \\
    \textbf{The third challenge is the high resolution of the images
    captured by UAVs.} Traditional methods for BAS and machine learning require preprocessing and post-processing which fail
    to achieve end-to-end segmentation.
        \end{block}
    \end{minipage}
    \hfill % This creates a horizontal fill, pushing the next mini page to the right.
    \begin{minipage}{0.5\linewidth} % 50% of the text width
        \begin{figure}
            \centering
            \includegraphics[scale=0.45]{figures/fig4.png}
        \end{figure}
    \end{minipage}
\end{frame}


\begin{frame}
    \frametitle{Useful Sentences of Function3}
    \begin{block}{}
        \begin{itemize}
            \item This research sheds new light on ...
            \item This study provides new insights into...
            \item The study offers some important insights into
            \item The present study fills a gap in the literature by ..
            \item Understanding the link between X and Y will help ..
            \item The present research explores, for the first time, the effects of ...
            \item The findings should make an important contribution to the field of ...
            \item This study provides an exciting opportunity to advance our knowledge of ..
            \item This study aims to contribute to this growing area of research by exploring ...
            \item Therefore, this study makes a major contribution to research on X by demonstrating ...
        \end{itemize}
    \end{block}
\end{frame}

\begin{frame}
    \frametitle{Function4: Stating the main contribution \& structure of the paper}

    \begin{block}{Role and content of the last paragraph}

        \begin{itemize}
            \item In computer-related articles, the last paragraph will summarize the main contributions of the article and explain the structure of the article's theme.
            \item It focuses on describing the structure of the paper. It serves as a guide to the entire framework of the essay, leading logically and clearly to the following.
            \item It is necessary to specify the section where the related work, experimental materials, methods, results, discussion and conclusions are to be found.
        \end{itemize}

    \end{block}

\end{frame}

\begin{frame}
    \frametitle{Example of function4}
    \begin{block}{Main contribution}
        In this paper, we address the challenges existing in the task of segmenting post-fire burned area using RS images. The major contributions {of} this paper are summarized as follows:

1) {An end-to-end network BASNet based on SOD is proposed to segment the burned area using high-resolution images acquired from UAV. BASNet effectively solves the problem that previous methods used for BAS cannot accurately detect the target in real time.}

2) The positioning and refinement modules are proposed to capture the multi-level feature information by fusing rich semantics with the information of spatial location and edge, which effectively {alleviate} the problem of segmenting the large burned regions.

3)  An adaptive loss function is introduced to enable the network to focus more on pixels near fine or explicit edges of burned regions and similar patterns between foreground and background in RS images, thus increasing the segmentation accuracy. 

4) A high-resolution dataset containing UAV acquired image samples and the corresponding pixel-wise annotations is created from Chongli District {for BAS.} The proposed BASNet consistently outperforms other state-of-the-art methods in the experiments.
    \end{block}
\end{frame}

\begin{frame}
    \frametitle{Example of function4}
    \begin{block}{Structure of the paper}
        The rest of this paper is organized as follows. 
        In Section II, we briefly review the related works of SOD and its applications in optical RS images and BAS.
         In Section III, we present the details of the proposed BASNet. 
         In Section IV, the experimental comparisons and ablation analysis are discussed. Finally, the conclusion is drawn in Section {V}.
    \end{block}
\end{frame}

\begin{frame}
    \frametitle{Useful Sentences of Function4}
    \begin{block}{}
        \begin{itemize}
            \item The rest of this paper is organized as follows.
            \item Section *** describes ***.
            \item Section *** presents the details of ****.
            \item Section *** provides ***.
            \item Some results are reported in section ***.
            \item Conclusion is drawn in section ***.
        \end{itemize}
    \end{block}
\end{frame}

\begin{frame}[plain]
    \vfill
    \centering
    \begin{beamercolorbox}[sep=4pt,center,colsep=-4bp,rounded=true,shadow=true]{title}
        \Huge  Some examples of featured introduction
    \end{beamercolorbox}
    \vfill
\end{frame}

\begin{frame}
    \textbf{In the opening paragraph, first summarize and elevate the problem.}
    \frametitle{Example 1}
    \begin{block}{}
        Unsupervised representation learning is highly successful in natural language processing, e.g., as shown by GPT
[50, 51] and BERT [12]. But supervised pre-training is still
dominant in computer vision, where unsupervised methods generally lag behind. The reason may stem from differences in their respective signal spaces. Language tasks
have discrete signal spaces (words, sub-word units, etc.)
for building tokenized dictionaries, on which unsupervised
learning can be based. Computer vision, in contrast, further
concerns dictionary building [54, 9, 5], as the raw signal is
in a continuous, high-dimensional space and is not structured for human communication (e.g., unlike words).
    \end{block}
    \textbf{Kaiming He,} Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 9729-9738
\end{frame}

\begin{frame}
    \frametitle{Example 2}

\textbf{Start with a direct illustration of the work I have done using a picture.}

    \begin{minipage}{0.4\linewidth} % 40% of the text width
        \begin{block}{}
            Consider Fig. 1, can we design object detectors beyond recognizing only base categories (e.g., toy)
    present in training labels and expand the vocabulary to detect novel categories (e.g., toy elephant)?
    In this paper, we aim to train an open-vocabulary object detector that detects objects in any novel
    categories described by text inputs, using only detection annotations in base categories.
        \end{block}
    \end{minipage}
    \hfill % This creates a horizontal fill, pushing the next mini page to the right.
    \begin{minipage}{0.5\linewidth} % 50% of the text width
        \begin{figure}
            \centering
            \includegraphics[scale=0.34]{figures/fig5.png}
        \end{figure}
    \end{minipage}
    
    Gu X, Lin T Y, Kuo W, et al. Open-vocabulary Object Detection via Vision and Language Knowledge Distillation[C]//International Conference on Learning Representations. 2021.

\end{frame}
%% normal frame
% \section{Bayesian Statistics Tutorial}
% \subsection{}

% \begin{frame}
%     \frametitle{Few Shot Classfication}
%     \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.3]{figures/fsc01.png}
%     \end{figure}

    
    % conditional probabilities:
    % $$
    % p(x|y) \coloneqq \frac{p(x,y)}{p(y)}
    % $$
    % the joint probalility of $x$ and $y$:
    % $$
    % p(x,y)=p(x|y)p(y)=p(y|x)p(x)
    % $$

    % \begin{block}{Theorem: Bayes Rule}
    % Denote by X and Y random variables then the following holds
    % $$
    % p(y|x)=\frac{p(x|y)p(y)}{p(x)}
    % $$
    % \end{block}

% \end{frame}

% \begin{frame}
%     \frametitle{Few Shot Classfication}
%     \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.3]{figures/fsc02.png}
%     \end{figure}

% \end{frame}



% \begin{frame}
%     \frametitle{How to solve the few-shot classification problem}
%        \textbf{ Basic Idea: Judging through similarity function}
%         \begin{figure}[htb]
%             \centering
%             \includegraphics[scale=0.22]{figures/fsc03.png}
%         \end{figure}
% \end{frame}

% \begin{frame}
%     \frametitle{How to solve the few-shot classification problem}
    
%      \begin{block}{Step 1: Pretraining}
%         \begin{itemize}
%             \item Pretrain a CNN on large-scale training data.
%             \item Use the CNN for feature extraction.
%         \end{itemize}
%     \end{block}
    
%     \begin{figure}[htb]
%     \centering
%     \begin{minipage}{0.5\textwidth}
%         \centering
%         \includegraphics[scale=0.25]{figures/fsc04.png}
%         % 如果你想为这张图片添加标题，可以使用 \caption{第一张图片}
%     \end{minipage}%
%     \begin{minipage}{0.5\textwidth}
%         \centering
%         \includegraphics[scale=0.1]{figures/fsc05.png} % 将another_image.png替换为您要插入的第二张图片的文件名
%         % 如果你想为这张图片添加标题，可以使用 \caption{第二张图片}
%     \end{minipage}
%     \end{figure}
% \end{frame}

% \begin{frame}
%     \frametitle{How to solve the few-shot classification problem}
    
%      \begin{block}{Step 2: Few-shot Prediction}
%         \begin{itemize}
%             \item Map images in the support set to feature vectors.
%             \item Obtain the mean feature vector of each class：$\boldsymbol{\mu}_1, \cdots, \boldsymbol{\mu}_k$.
%             \item Compare the feature of query with $\boldsymbol{\mu}_1, \cdots, \boldsymbol{\mu}_k$.
%         \end{itemize}
%     \end{block}
    
%     \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.25]{figures/fsc06.png}
%     \end{figure}
% \end{frame}


% \begin{frame}
%     \frametitle{How to convert a classification problem into a segmentation problem}
%      \begin{block}{One Shot Segmentation Problem Setting}
% Let the support set $S=\left\{\left(I_s^i, Y_s^i(l)\right)\right\}_{i=1}^k$ be a small set of $k$ image-binary mask pairs where $Y_s^i \in L_{\text {test }}^{H \times W}$ is the segmentation annotation for image $I_s^i$ and $Y_s^i(l)$ is the mask of the $i^{t h}$ image for the semantic class $l \in L_{\text {test }}$. The goal is to learn a model $f\left(I_q, S\right)$ that, when given a support set $S$ and query image $I_q$, predicts a binary mask $\hat{M}_q$ for the semantic class $l$. 
%     \end{block}


        
%         \begin{block}{Maximize the log likelihood of the ground-truth mask}
%         $$
%         \mathcal{L}(\eta, \zeta)=\underset{S, I_q, M_q \sim D_{\text {train }}}{\mathbb{E}}\left[\sum_{m, n} \log p_{\eta, \zeta}\left(M_q^{m n} \mid I_q, S\right)\right]
%         $$
%         \end{block}

%         \begin{table}[]
%         \begin{tabular}{|l|l|l|}
%         \hline
%                       & X     & Y     \\ \hline
%         support image & H*W*3 & H*W*1 \\ \hline
%         query image   & H*W*3 &       \\ \hline
%         \end{tabular}
%         \end{table}
        
%     % \begin{figure}[htb]
%     %     \centering
%     %     \includegraphics[scale=0.25]{figures/fss01.png}
%     % \end{figure}

%     \footnotetext{Shaban, Amirreza, et al. "One-shot learning for semantic segmentation." arXiv preprint arXiv:1709.03410}
    
% \end{frame}

% \begin{frame}{The dataset for one shot Segmentation}

%  \begin{block}{$PASCAL-5^i$}
%       From $L$, the set of twenty semantic classes in PASCALVOC, we sample five and consider them as the test label-set $L_{t e s t}=\{4 i+1, \ldots, 4 i+5\}$, with $i$ being the fold number, and the remaining fifteen forming the training label-set $L_{\text {train }}$. Test and training class names are shown in Table. We form the training set $D_{\text {train }}$ by including all image-mask pairs from PASCALVOC and SDS training sets that contain at least one pixel in the semantic mask from the label-set $L_{\text {train }}$. The masks in $D_{\text {train }}$ are modified so that any pixel with a semantic class $\neq L_{\text {train }}$ is set as the background class $l_{\varnothing}$. 
%  \end{block}

%      \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.35]{figures/fss02.png}
%     \end{figure}

%     \footnotetext{Shaban, Amirreza, et al. "One-shot learning for semantic segmentation." arXiv preprint arXiv:1709.03410}
% \end{frame}

% \begin{frame}{How to solve the few-shot segmentation problem}

%  \begin{block}{ Model Architecture}
%      The conditioning branch receives an image-label pair and produces a set of parameters $\{w, b\}$ for the logistic regression layer $c(\cdot, w, b)$. The segmentation branch is an FCN that receives a query image as input and outputs strided features of conv-fc7. The predicted mask is generated by classifying the pixel-level features through $c(\cdot, w, b)$, which is then upsampled to the original size.
%  \end{block}

%      \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.3]{figures/fss01.png}
%     \end{figure}

%     \footnotetext{Shaban, Amirreza, et al. "One-shot learning for semantic segmentation." arXiv preprint arXiv:1709.03410}
% \end{frame}

% \begin{frame}{How to solve the few-shot segmentation problem}
%     \textbf{Experimental results}
%         \begin{figure}[htb]
%     \centering
%     \begin{minipage}{0.5\textwidth}
%         \centering
%         \includegraphics[scale=0.25]{figures/fss03.png}
%         % 如果你想为这张图片添加标题，可以使用 \caption{第一张图片}
%     \end{minipage}%
%     \begin{minipage}{0.5\textwidth}
%         \centering
%         \includegraphics[scale=0.2]{figures/fss04.png} % 将another_image.png替换为您要插入的第二张图片的文件名
%         % 如果你想为这张图片添加标题，可以使用 \caption{第二张图片}
%     \end{minipage}
%     \end{figure}
% \end{frame}

% \begin{frame}{Current Methods}
%     \begin{enumerate}
%         \item PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment,(ICCV 2019)  metric learning
%         \item PFENet: Prior Guided Feature Enrichment Network for Few-Shot Segmentation, (TPAMI, 2022) fixed backbone
%         \item BAM: Learning What Not to Segment: A New Perspective on Few-Shot Segmentation, (CVPR2022 oral)
%     \end{enumerate}
% \end{frame}


% \begin{frame}{PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment,(ICCV 2019)}
% \textbf{1.Feature map extraction：}以2-way 1-shot为例，首先通过权重相同的空洞卷积的VGG-16网络作为backbone来提取特征图\\
% \textbf{2.prototype learning：}通过mask average pooling得到Support Image中各个类的prototype。

%      \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.2]{figures/fss06.png}
%     \end{figure}
% \vspace{-0.3cm}
%     $$
%     p_c=\frac{1}{K} \sum_k \frac{\sum_{x, y} F_{c, k}^{(x, y)} I\left[M_{c, k}^{(x, y)}=c\right]}{\sum_{x, y} I\left[M_{c, k}^{(x, y)}=c\right]}
%     $$


% \end{frame}

% \begin{frame}{PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment,(ICCV 2019)}
% \textbf{3.metric learning：}计算每个原型与每个空间位置的查询特征之间的余弦距离，根据最近邻原则对查询图像逐像素标注（进行分割），$L_{seg}$计算了分割结果与ground truth mask之间的损失 


%      \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.2]{figures/fss06.png}
%     \end{figure}

% \vspace{-0.3cm}

% \begin{minipage}{0.4\linewidth} % 40% of the text width
%         $$
%     \tilde{M}_{q ; j}^{(x, y)}=\frac{\exp \left(-\alpha d\left(F_q^{(x, y)}, p_j\right)\right)}{\sum_{p_j \in \mathcal{P}} \exp \left(-\alpha d\left(F_q^{(x, y)}, p_j\right)\right)}
%     $$
% \end{minipage}
% \hfill % This creates a horizontal fill, pushing the next mini page to the right.
% \begin{minipage}{0.5\linewidth} % 50% of the text width

% $$
% \hat{M}_q^{(x, y)}=\underset{j}{\arg \max } \tilde{M}_{q ; j}^{(x, y)}
% $$
% \vspace{-0.3cm}
%  $$
% \mathcal{L}_{\text {seg }}=-\frac{1}{N} \sum_{x, y} \sum_{p_j \in \mathcal{P}} I \left[M_q^{(x, y)}=j\right] \log \tilde{M}_{q ; j}^{(x, y)}
%     $$

% \end{minipage}


% \end{frame}

% \begin{frame}{PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment,(ICCV 2019)}
%     \textbf{4. Prototype alignment regularization(PAR)：}将查询图像的分割预测后的mask与查询特征融合，并进行了相应的mask average pooling，得到另一组原型。之后用此原型来预测支持图像的mask。实现PAR的整个过程可以看作是交换支持和查询集

%      \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.2]{figures/fss06.png}
%     \end{figure}
%     \vspace{-0.3cm}
% \begin{minipage}{0.4\linewidth} % 40% of the text width
%         $$
% \tilde{M}_{c, k ; j}^{(x, y)}=\frac{\exp \left(-\alpha d\left(F_{c, k}^{(x, y)}, \bar{p}_j\right)\right)}{\sum_{\bar{p}_j \in\left\{\bar{p}_c, \bar{p}_{\mathrm{bg}}\right\}} \exp \left(-\alpha d\left(F_{c, k}^{(x, y)}, \bar{p}_j\right)\right)}
%     $$
% \end{minipage}
% \hfill % This creates a horizontal fill, pushing the next mini page to the right.
% \begin{minipage}{0.5\linewidth} % 50% of the text width

% \begin{equation*}
% \scalebox{0.8}{$\mathcal{L}_{\mathrm{PAR}}=-\frac{1}{C K N} \sum_{c, k, x, y} \sum_{p_j \in \mathcal{P}} \mathbb{1}\left[M_q^{(x, y)}=j\right] \log \tilde{M}_{q ; j}^{(x, y)}$}
% \end{equation*}


% \vspace{-0.4cm}
%  $$
% \mathcal{L}=\mathcal{L}_{\mathrm{seg}}+\lambda \mathcal{L}_{\mathrm{PAR}}
%     $$

% \end{minipage}
    
% \end{frame}

% \begin{frame}{PFENet: Prior Guided Feature Enrichment Network for Few-Shot Segmentation, (TPAMI 2022)}
%     \begin{block}{Motivation}
%         \begin{itemize}
%             \item inappropriate use of high-level semantic information of training classes
%             \item spatial inconsistency between query and support targets
%         \end{itemize}
%     \end{block}

%      \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.32]{figures/fss07.png}
%     \end{figure}
    
% \end{frame}


% \begin{frame}{PFENet: Prior Guided Feature Enrichment Network for Few-Shot Segmentation, (TPAMI 2022)}

%     \textbf{a training-free prior mask generation method }
%      \begin{figure}[htb]
%         \centering
%         \includegraphics[scale=0.2]{figures/fss08.png}
%     \end{figure}
% \vspace{-0.4cm}
%     \begin{minipage}{0.4\linewidth} % 40% of the text width
%         $$
% X_Q=\mathcal{F}\left(I_Q\right), \quad X_S=\mathcal{F}\left(I_S\right) \odot M_S
%     $$
%  $$
%     \cos \left(x_q, x_s\right)=\frac{x_q^T x_s}{\left\|x_q\right\|\left\|x_s\right\|} \quad q, s \in\{1,2, \ldots, h w\}
%      $$
% \end{minipage}
% \hfill % This creates a horizontal fill, pushing the next mini page to the right.
% \begin{minipage}{0.5\linewidth} % 50% of the text width

% \begin{equation*}
% \begin{aligned}
% c_q & =\max _{s \in\{1,2, \ldots, h w\}}\left(\cos \left(x_q, x_s\right)\right), \\
% C_Q & =\left[c_1, c_2, \ldots, c_{h w}\right] \in \mathbb{R}^{h w \times 1} .
% \end{aligned}
% \end{equation*}


% \vspace{-0.4cm}
%  $$
% Y_Q=\frac{Y_Q-\min \left(Y_Q\right)}{\max \left(Y_Q\right)-\min \left(Y_Q\right)+\epsilon}
%     $$

% \end{minipage}
    
% \end{frame}



% \begin{frame}{PFENet: Prior Guided Feature Enrichment Network for Few-Shot Segmentation, (TPAMI 2022)}
%     \textbf{Feature Enrichment Module (FEM)}
%         \begin{figure}[htb]
%     \centering
%     \begin{minipage}{0.5\textwidth}
%         \centering
%         \includegraphics[scale=0.2]{figures/fss09.png}
%         % 如果你想为这张图片添加标题，可以使用 \caption{第一张图片}
%     \end{minipage}%
%     \begin{minipage}{0.5\textwidth}
%         \centering
%         \includegraphics[scale=0.3]{figures/fss10.png} % 将another_image.png替换为您要插入的第二张图片的文件名
%         % 如果你想为这张图片添加标题，可以使用 \caption{第二张图片}
%     \end{minipage}
%     \end{figure}
% \end{frame}

% \begin{frame}{PFENet: Prior Guided Feature Enrichment Network for Few-Shot Segmentation, (TPAMI 2022)}
%     \begin{figure}
%         \centering
%         \includegraphics[scale=0.3]{figures/fss13.png}

%     \end{figure}
% \end{frame}

% \begin{frame}{PFENet: Prior Guided Feature Enrichment Network for Few-Shot Segmentation, (TPAMI 2022)}
%     \begin{figure}
%         \centering
%         \includegraphics[scale=0.35]{figures/fss11.png}

%     \end{figure}
% \end{frame}

% \begin{frame}{PFENet: Prior Guided Feature Enrichment Network for Few-Shot Segmentation, (TPAMI 2022)}
%     \begin{figure}
%         \centering
%         \includegraphics[scale=0.25]{figures/fss12.png}

%     \end{figure}
% \end{frame}

% \begin{frame}{The current mainstream few-shot segmentation Method}
% \begin{minipage}{0.4\linewidth} % 40% of the text width
%     \includegraphics[scale=0.25]{figures/fss05.png}
% \end{minipage}
% \hfill % This creates a horizontal fill, pushing the next mini page to the right.
% \begin{minipage}{0.5\linewidth} % 50% of the text width


% \end{minipage}


%  \footnotetext{Learning What Not to Segment: A New Perspective on Few-Shot Segmentation, (CVPR2022 oral)}
% \end{frame}


% \begin{frame}
%     \frametitle{An Example}

%     \begin{parchment}[Question]
%         Assume that a patient would like to have such a test carried out on him. The physician recommends a test which is guaranteed to detect HIV-positive whenever a patient is infected. On the other hand, for healthy patients it has a $1\%$ error rate. That is, with probability 0.01 it diagnoses a patient as HIV-positive even when he is, HIV-negative. \uwave{Moreover, assume that $0.15\%$ of the population is infected.}
%         \\[2ex]
%         Now the patient has the test carried out and the test returns HIV-negative. In this case, logic implies that he is healthy, since the test has $100\%$ detection rate. In the converse case things are not quite as straightforward.
%         \\[2ex]
%         So what's the $p(X = \mathtt{HIV+}|T = \mathtt{HIV+})$?
%     \end{parchment}
    
% \end{frame}

% \begin{frame}
%     \frametitle{An Example}

%     \center{
%     \begin{tabular}{ c | c c }
%         $p(t|x)$ & $X = \mathtt{HIV-}$ & $X=\mathtt{HIV+}$ \\
%         \hline
%         $T=\mathtt{HIV-}$ & 0.99 & 0 \\
%         $T=\mathtt{HIV+}$ & 0.01 & 1
%     \end{tabular}
%     }

%     $$
%     p(X = \mathtt{HIV+}) = 0.0015
%     $$
% \end{frame}

% \begin{frame}
%     \frametitle{An Example}

%     By Bayes rule we may write
%     $$
%     p(X = \mathtt{HIV+}|T=\mathtt{HIV+}) = \frac{p(T=\mathtt{HIV+}|X=\mathtt{HIV+})p(X=\mathtt{HIV+})}{p(T=\mathtt{HIV+})}
%     $$

%     While we know all terms in the numerator, $p(T = \mathtt{HIV+})$itself is unknown. That said, it can be computed via
%     \begin{align}
%     \nonumber p(T=\mathtt{HIV+}) &= \sum_{x \in \{\mathtt{HIV+}, \mathtt{HIV-}\}}p(T=\mathtt{HIV+},x) \\
%     \nonumber &= \sum_{x \in \{\mathtt{HIV+}, \mathtt{HIV-}\}}p(T=\mathtt{HIV+}|x)p(x) \\
%     \nonumber &= 1.0 \cdot 0.0015 + 0.01 \cdot 0.9985
%     \end{align}

%     Substituting back into the conditional expression yields
%     $$
%     p(X = \mathtt{HIV+}|T=\mathtt{HIV+}) = \frac{1.0 \cdot 0.0015}{1.0 \cdot 0.0015 + 0.01 \cdot 0.9985} = 0.1306
%     $$

% \end{frame}


% \begin{frame}
%     \frametitle{How can we improve the diagnosis}

%     % Define block styles
%     \tikzset{
%         grayCircle/.style = {
%             draw,
%             circle,
%             node distance=2.5cm,
%             minimum size=1.5cm,
%             fill=black!20
%         }
%     }

%     \center
%     \begin{tikzpicture}
%         \node[grayCircle] (age) {age};
%         \node[grayCircle, right of=age, style={fill=none}] (x) {x};
%         \node[grayCircle, right of=x, yshift=1.25cm] (t1) {test 1};
%         \node[grayCircle, below of=t1] (t2) {test 2};
%         \draw[->, >=latex] (age) -- (x);
%         \draw[->, >=latex] (x) -- (t1);
%         \draw[->, >=latex] (x) -- (t2);
%     \end{tikzpicture}

%     \begin{figure}
%         \caption{A graphical description of our HIV testing scenario. Knowing the age of the patient influences our prior on whether the patient is HIV positive (the random variable X). The outcomes of the tests 1 and 2 are independent of each other given the status X. We observe the shaded random variables (age, test 1, test 2) and would like to infer the un-shaded random variable X.}
%     \end{figure}

% \end{frame}


% \begin{frame}
%     \frametitle{How can we improve the diagnosis}

%     \begin{parchment}[Including additional observed random variables]
%     One way is to obtain further information about the patient and to use this in the diagnosis. For instance, information about his age is quite useful. Suppose the patient is 35 years old. In this case we would want to compute $p(X = \mathtt{HIV+}|T = \mathtt{HIV+}, A = 35)$ where the random variable A denotes the age.
%     \end{parchment}

%     The corresponding expression yields:
%     $$
%     \frac{p(T=\mathtt{HIV+}|X=\mathtt{HIV+},A)p(X=\mathtt{HIV+}|A)}{p(T=\mathtt{HIV+}|A)}
%     $$
% \end{frame}


% \begin{frame}
%     \frametitle{How can we improve the diagnosis}

%     We may assume that the test is independent of the age of the patient, i.e.
%     $$
%     p(t|x,a) = p(t|x)
%     $$

%     What remains therefore is $p(X = \mathtt{HIV+}|A)$. Recent US census data pegs this number at approximately $0.9\%$. 
%     \begin{align}
%     \nonumber & p(X = \mathtt{H+}|T = \mathtt{H+}, A) = \frac{p(T=\mathtt{H+}|X=\mathtt{H+},A)p(X=\mathtt{H+}|A)}{p(T=\mathtt{H+}|A)} \\
%     \nonumber &= \frac{p(T=\mathtt{H+}|X=\mathtt{H+},A)p(X=\mathtt{H+}|A)}{p(T=\mathtt{H+}|X=\mathtt{H+},A)p(X=\mathtt{H+}|A) + p(T=\mathtt{H+}|X=\mathtt{H-},A)p(X=\mathtt{H-}|A)} \\
%     \nonumber & = \frac{p(T=\mathtt{H+}|X=\mathtt{H+})p(X=\mathtt{H+}|A)}{p(T=\mathtt{H+}|X=\mathtt{H+})p(X=\mathtt{H+}|A) + p(T=\mathtt{H+}|X=\mathtt{H-})p(X=\mathtt{H-}|A)} \\
%     \nonumber & = \frac{1 \cdot 0.009}{1 \cdot 0.009 + 0.01 \cdot 0.991} = 0.48
%     \end{align}

% \end{frame}


% \begin{frame}
%     \frametitle{How can we improve the diagnosis}

%     \begin{parchment}[Multiple measurements]
%     A second tool in our arsenal is the use of multiple measurements. After the first test the physician is likely to carry out a second test to confirm the diagnosis. We denote by $T_1$ and $T_2$ (and $t_1$,$t_2$ respectively) the two tests. Obviously, what we want is that $T_2$ will give us an "independent" second opinion of the situation.
%     \\[2ex]
%     What we want is that the diagnosis of $T_2$ is independent of that of $T_2$ given the health status X of the patient. This is expressed as
%     $$
%     p(t_1,t_2|x) = p(t_1|x)p(t_2|x)
%     $$
%     which are commonly referred to as \uwave{conditionally independent}.

%     \end{parchment}
% \end{frame}

% \begin{frame}
%     \frametitle{How can we improve the diagnosis}

%     we assume that the statistics for $T_2$ are given by
%     \center{
%     \begin{tabular}{ c | c c }
%         $p(t_2|x)$ & $X = \mathtt{HIV-}$ & $X=\mathtt{HIV+}$ \\
%         \hline
%         $T_2=\mathtt{HIV-}$ & 0.95 & 0.01 \\
%         $T_2=\mathtt{HIV+}$ & 0.05 & 0.99 
%     \end{tabular}
%     }

%     \flushleft
%     for $t_1 = t_2 = \mathtt{HIV+}$ we have
%     \begin{align}
%     \nonumber & p(X=\mathtt{H+}|T_1=\mathtt{H+},T_2=\mathtt{H+}) \\
%     \nonumber &= \frac{p(T_1=\mathtt{H+}, T_2=\mathtt{H+}|X=\mathtt{H+})p(X=\mathtt{H+}|A)}{p(T_1=\mathtt{H+}, T_2=\mathtt{H+}|A)} \\
%     \nonumber &= p(T_1=\mathtt{H+}|X=\mathtt{H+})p(T_2=\mathtt{H+}|X=\mathtt{H+})p(X=\mathtt{H+}|A) \;/ \\
%     \nonumber & p(T_1=\mathtt{H+}|X=\mathtt{H+})p(T_2=\mathtt{H+}|X=\mathtt{H+})p(X=\mathtt{H+}|A) \\
%     \nonumber & + p(T_1=\mathtt{H+}|X=\mathtt{H-})p(T_2=\mathtt{H+}|X=\mathtt{H-})p(X=\mathtt{H-}|A) \\
%     % \nonumber & p(T_{1,H+}|X_{H+})p(T_{2,H+}|X_{H+})p(X_{H+}|A) \\
%     % \nonumber & + p(T_{1,H+}|X_{H-})p(T_{2,H+}|X_{H-})p(X_{H-}|A) \\
%     \nonumber &= \frac{1 \cdot 0.99 \cdot 0.009}{1 \cdot 0.99 \cdot 0.009 + 0.01 \cdot 0.05 \cdot 0.991} = 0.95
%     \end{align}
% \end{frame}

\end{CJK}
\end{document}